Abstract
Agriculture, which is regarded as the fundamental basis of the economy, has a major impact on a nation’s economic growth and its GDP (gross domestic product). Agriculture is most commonly associated with the production of essential crops. The farmers undergo several challenges in each stage of crop production, including soil preparation, seed selection, spotting of disease, irrigation problems, predicting yields, weed control, etc. This study presents an in-depth study of the recent advancements in the agricultural sector using machine learning (ML) techniques. Machine learning is nowadays used in almost every stage of the agricultural process. The study presents a detailed review of the different machine learning methods employed in agriculture, including the advantages of using the techniques and the challenges incurred in their adoption. The study further provides a comparison and contrast of different machine learning techniques to make it more informative for future researchers.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Trends in Computer Science and Smart Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
